Target tracking method and expansion truncation no-trace Kalman filtering method and device

Abstract

The invention discloses a target tracking method and system and an expansion truncation no-trace Kalman filtering method and device. The expansion truncation no-trace Kalman filtering method comprises the steps that an original prior probability density function is obtained according to no-trace conversion; a first posterior probability density function is obtained according to the original prior probability density function; the original prior probability density function is corrected according to the statistics linear regression theory and a target observation vector of the current target observation moment so that the corrected prior probability density function can be obtained; a second posterior probability density function is obtained according to the corrected prior probability density function; a combined posterior probability density function is obtained according to the original prior probability density function and the second posterior probability density function. According to the Kalman filtering method, the problem that the observation function does not have the only inverse function can be solved, prior distribution variances of target states are effectively reduced, state upgrading is carried out in a self-adaptation mode according to the precision of the observation information, the filtering precision is effectively improved, and the practicality is high.

Description

technical field [0001] The invention relates to the field of nonlinear filtering, in particular to a target tracking method and system, and an extended truncated unscented Kalman filtering method and device. Background technique [0002] In scientific fields such as navigation and guidance systems, tracking the channel state information of rapidly changing wireless channels, and tracking the real-time position of aircraft, it is often necessary to use filtering technology to achieve real-time tracking of targets. The following filtering methods are often used in the prior art: the first one is the Kalman filter (KF) proposed by Kalman in 1960, which can deal with linear systems with Gaussian distribution noise to obtain the minimum mean square error estimation of the system state RMMSE; the second is to use sequential Monte Carlo method (SMC), such as particle filter (PF); the third is the algorithm of Kalman filter type, such as extended Kalman filter, unscented Kalman filt...

AI Technical Summary

Problems solved by technology

[0004] The technical problem mainly solved by the present invention is to provide a target tracking method, system and extended truncated unscented Kalman filter method and device, which can solve the problem that the observation function does not have a unique inverse function, effectively reduce the variance of the prior distribution of the target state, and automatically Adaptively update the status according to the accuracy of the observation information, effectively improve the filtering accuracy and have high practicability

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